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Physics of day-to-day network flow dynamicsAuthor-Name: Xiao, Feng

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  • Yang, Hai
  • Ye, Hongbo

Abstract

This paper offers a new look at the network flow dynamics from the viewpoint of physics by demonstrating that the traffic system, in terms of the aggregate effects of human behaviors, may exhibit like a physical system. Specifically, we look into the day-to-day evolution of network flows that arises from travelers’ route choices and their learning behavior on perceived travel costs. We show that the flow dynamics is analogous to a damped oscillatory system. The concepts of energies are introduced, including the potential energy and the kinetic energy. The potential energy, stored in each link, increases with the traffic flow on that link; the kinetic energy, generated by travelers’ day-to-day route swapping, is proportional to the square of the path flow changing speed. The potential and kinetic energies are converted to each other throughout the whole flow evolution, and the total system energy keeps decreasing owing to travelers’ tendency to stay on their current routes, which is analogous to the damping of a physical system. Finally, the system will approach the equilibrium state with minimum total potential energy and zero kinetic energy. We prove the stability of the day-to-day dynamics and provide numerical experiments to elucidate the interesting findings.

Suggested Citation

  • Yang, Hai & Ye, Hongbo, 2016. "Physics of day-to-day network flow dynamicsAuthor-Name: Xiao, Feng," Transportation Research Part B: Methodological, Elsevier, vol. 86(C), pages 86-103.
  • Handle: RePEc:eee:transb:v:86:y:2016:i:c:p:86-103
    DOI: 10.1016/j.trb.2016.01.016
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    Cited by:

    1. Crawford, F. & Watling, D.P. & Connors, R.D., 2017. "A statistical method for estimating predictable differences between daily traffic flow profiles," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 196-213.
    2. Xiaomei Zhao & Chunhua Wan & Jun Bi, 2019. "Day-to-Day Assignment Models and Traffic Dynamics Under Information Provision," Networks and Spatial Economics, Springer, vol. 19(2), pages 473-502, June.
    3. Qixiu Cheng & Zhiyuan Liu & Feifei Liu & Ruo Jia, 2017. "Urban dynamic congestion pricing: an overview and emerging research needs," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 3-18, August.
    4. Chen, Jin-Yong & Jiang, Rui & Li, Xin-Gang & Hu, Mao-Bin & Jia, Bin & Gao, Zi-You, 2019. "Morning commute problem with queue-length-dependent bottleneck capacity," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 184-215.
    5. Liu, Wei & Geroliminis, Nikolas, 2017. "Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 162-179.
    6. Han, Linghui & Zhu, Chengjuan & Wang, David Z.W. & Sun, Huijun & Tan, Zhijia & Meng, Meng, 2019. "Discrete-time dynamic road congestion pricing under stochastic user optimal principle," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 24-36.
    7. Guo, Ren-Yong & Szeto, W.Y., 2018. "Day-to-day modal choice with a Pareto improvement or zero-sum revenue scheme," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 1-25.
    8. Guo, Ren-Yong & Yang, Hai & Huang, Hai-Jun & Li, Xinwei, 2018. "Day-to-day departure time choice under bounded rationality in the bottleneck model," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 832-849.
    9. Yildirimoglu, Mehmet & Ramezani, Mohsen, 2020. "Demand management with limited cooperation among travellers: A doubly dynamic approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 267-284.

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